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diff --git a/tutorials/module_4/4.0 Outline.md b/tutorials/module_4/4.0 Outline.md
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--- a/tutorials/module_4/4.0 Outline.md
+++ b/tutorials/module_4/4.0 Outline.md
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 # Module 4: Outline
 
 1. Introduction to Data and Scientific Datasets
-	a. What is scientfic data
+	a. What is scientific data
 	b. Data Processing flow work
 	c. Intro to Pandas
 		d. Manipulating data frames
-	e. Problem: Create a daraframe from Numpy arrays
+	e. Problem 1: Create a dataframe from Numpy arrays
+	f. Problem 2: Selecting data from a dataframe to calculate work done.
 
 2. Interpreting Data
 	a. Understanding your data
 	b. Purpose
 	c. Composition
 	d. Color
-	e. Problem 1: Composing or fixing a plot
+	e. Problem 1: Composing or fixing a plot. Apply PCC
 	f. Data don't lie
-	g. Problem 2: Misleading plots
+	g. Problem 2: Misleading plots by changing axis limits or omitting context. Explain *why* it's misleading.
 
 3. Importing, Exporting and Managing Data
 	a. File types
 	b. Importing spreadsheets with pandas
 	c. Handling header, units and metadata
 	d. Writing and editing data in pandas
-	e. Problem: Importing time stamped data
+	e. Problem: Importing time stamped data pressure and temperature data. Convert timestaps to datetime and plot timperature vs. time
+	f. Problem: Add metadata () [TBD]
 
-4. Statistical Analysis
+4. Statistical Analysis I
 	a. Engineering Models
 	b. Statistics Review
 	c. Statistics function in python (Numpy and Pandas describe)
 	d. Statistical Distributions
 	e. Spectrocopy (basics) 
-	f. Problem: Statistical tools in Spectroscopy readings
+	f. Problem: Statistical tools in Spectroscopy readings (intensity vs wavelangth) to compute mean, variance and detect outliers.
+	g. Problem 2: Fit a Gaussian distribution to the same data and overlay it on the histogram.
 
-5. Statistical Analysis
+5. Statistical Analysis II: Regression and Smoothing
 	a. Linear Square Regression and Line of Best Fit
-		b. Linear
-		c. Exponential and Power functions
-		d. Polynomial**m 2:** From the DataFrame, add a
+		b. Linear, Exponential and Power functions
+		d. Polynomial
 		e. Using scipy
 		f. How well did we do? (R and R^2)
-	g. Extrapolation
-	h. Moving average
+	g. Extrapolation and limitations
+	h. Moving averages
+	i. Problem 1: Fit a linear and polynomial model to stress-strain data. Compute R^2 and discuss which model fits better.
+	j. Problem 2:  Apply a moving average to noisy temperature data and ocmpare ra vs. smoothed signals.
 
 6. Data Filtering and Signal Processing
-	
+	a. What is it and why it matters - noise vs. signal
+	b. Moving average and window functions
+	c. Frequency domain basics (sampling rate, Nyquist frequency)
+	d. Fourier transform overiew (numpy.fft, scipy.fft)
+	e. Low-pass and high-pass filters (scipy.singla.butter, filtfilt)
+	f. Example: Removing high-frequency noise from a displacement signal
+	g. Example: Removing noise from an image to help for further analysis (PIV)
+	h. Problem 1: Generate a synthetic signal (sum of two sine waves+random noise). Apply a moving average and FFT to show frequency components.)
+	i. Problem 2: Design a Butterworkth low-pass filter to isolate the funcamental frequency of a vibration signal (e.g. roating machinery). Plot before and after.
+
 7. Data Visualization and Presentation
-	a. Problem: Using pandas to plot spectroscopy data from raw data.
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+	a. Review of PCC framework
+	b. Plotting with Pandas and Matplotlib
+	c. Subplots, twin axes, and annotations
+	d. Colomaps and figure aesthetics
+	e. Exporitn gplots for reports (DPI, figure size)
+	f.  Creating dashboards or summary figures
+	g. Problem 1: Using pandas to plot spectroscopy data from raw data. Add labels, units, title, and annotations for peaks
+	h. Problem 2: Create a multi-panel figure showing raw data, fitted curve, and residuals. Format with consistent style, legend and color scheme for publication-ready quality.
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